Learning-based testing is a testing paradigm that combines model-based testing with machine learning algorithms to automate the modeling of the SUT, test case generation, test case execution and verdict construction. A tool that implements LBT been developed at the CSC school at KTH called LBTest. LBTest utilizes machine learning algorithms with off-the-shelf equivalence- and model-checkers, and the modeling of user requirements by propositional linear temporal logic. In this study, it is be investigated whether LBT may be suitable for testing a micro bus architecture within an embedded telecommunication device. Furthermore ideas to further automate the testing process by designing a data model to automate user requirement generation are explored. / Inlärningsbaserad testning är en testningsparadigm som kombinerar model-baserad testning med maskininlärningsalgoritmer för att automatisera systemmodellering, testfallsgenering, exekvering av tester och utfallsbedömning. Ett verktyg som är byggt på LBT är LBTest, utvecklat på CSC skolan på KTH. LBTest nyttjar maskininlärningsalgoritmer med färdiga ekvivalent- och model-checkers, och modellerar användarkrav med linjär temporal logik. I denna studie undersöks det om det är lämpat att använda LBT för att testa en mikrobus arkitektur inom inbyggda telekommunikationsenheter. Utöver det undersöks även hur testprocessen skulle kunna ytterligare automatiseras med hjälp av en data modell för att automatisera generering av användarkrav.

This thesis proposes a collaboration framework for the Web of Things based on the concepts of Service-oriented Architecture and integrated with semantic web technologies to offer new possibilities in terms of efficient asset management during operations requiring multi-actor collaboration. The motivation for the project comes from the rise in disasters where effective cross-organisation collaboration can increase the efficiency of critical information dissemination. Organisational boundaries of participants as well as their IT capability and trust issues hinders the deployment of a multi-party collaboration framework, thereby preventing timely dissemination of critical data. In order to tackle some of these issues, this thesis proposes a new collaboration framework consisting of a resource-based data model, resource-oriented access control mechanism and semantic technologies utilising the Semantic Sensor Network Ontology that can be used simultaneously by multiple actors without impacting each other’s networks and thus increase the efficiency of disaster management and relief operations. The generic design of the framework enables future extensions, thus enabling its exploitation across many application domains. The performance of the framework is evaluated in two areas: the capability of the access control mechanism to scale with increasing number of devices, and the capability of the semantic annotation process to increase in efficiency as more information is provided. The results demonstrate that the proposed framework is fit for purpose.

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本論文的研究主軸圍繞於自我迴歸模型的估計與推論上。文獻上自我迴歸模型的估計多直接採用最小平方法, 但此估計方式卻有兩個缺點:(一)當序列具單根時,最小平方估計式的漸近分配為非正規型態,因此檢定時需透過電腦模擬得到臨界值;(二)最小平方估計式雖具一致性,但卻有嚴重的有限樣本偏誤問題。有鑑於此,我們提出一種「二階差分轉換估計式」,並證明該估計式的偏誤遠低於前述最小平方估計式,且在序列為粧定與具單根的環境下具有相同的漸近常態分配。此外,二階差分轉換估計式相當適合應用於固定效果追蹤資料模型,而據以形成的追蹤資料單根檢定在序列較短的情況下仍有不錯的檢定力。 本論文共分四章,茲分別簡單說明如下: 第1章為緒論,回顧文獻上估計與推論自我回歸模型時的問題,並說明本論文的研究目標。估計自我迴歸模型的傳統方式是直接採取最小平方法,但在序列具單根的情況下由於訊息不隨時間消逝而快速累積,使估計式的收斂速度高於序列為恒定的情況。不過,這也導致最小平方估計式的漸近分配為非標準型態,並使得進行假設檢定前必須先透過電腦模擬來獲得臨界值。其次,最小平方估計式雖具一致性,但在有限樣本下卻是偏誤的。實證上, 樣本點不多是研究者時常面臨的窘境,並使得小樣本偏誤程度格外嚴重。本章中透過對前述問題形成因素的瞭解,說明解決與改善的方法,亦即我們提出的「二階差分轉換估計式」。 第2章主要目的在於推導二階差分轉換估計式之有限樣本偏誤。我們亦推導了多階差分自我迴歸模型下二階段最小平方估計式(two stage least squares, 2SLS)與 Phillips andHan (2008)採用的一階差分轉換估計式之偏誤,以同時進行比較。本章理論與模擬結果皆顯示,一階與二階差分轉換估許式與2SLS之 $T^{−1}$ 階偏誤程度皆低於以最小平方法估計原始準模型(level model)的偏誤,其中 T 為時間序列長度。另外,一階差分轉換估計式與二階差分轉換估計式在 $T^{−1}$ 階偏誤上,分別與一階和二階差分模型下2SLS相同,但兩估計式的相對偏誤程度則因自我相關係數的大小而互有優劣。同時,我們發現估計高於二階的差分模型對小樣本偏誤並無法有更進一步的改善。最後,即使在樣本點不多的情況下,本章所推導的偏誤理論對於實際偏誤仍有良好的近似能力。 第3章主要目的在於發展二階差分轉換估計式之漸近理論。與 Phillips and Han (2008) 採用之一階差分轉換估計式相似的是,該估計式在序列為恒定與具單根的情況下收斂速度相同,並有漸近常態分配的優點。值得注意的是, 二階差分轉換估計式的漸近分配為 N(0,2),不受任何未知參數的影響。另外,當序列呈現正自我相關時,二階差分轉換估計式相較於一階差分轉換估計式具有較小的漸近變異數,進而使得據以形成的檢定統計量有較佳的對立假設偵測能力。最後, 誠如 Phillips and Han (2008) 所述,由於差分過程消除了模型中的截距項,使得此類估計方法在固定效果的動態追蹤資料模型(dynamic panel data model with fixed effect) 具相當的發展與應用價值。 本論文第4 章進一步將二階差分轉換估計式推展至固定效果的動態追蹤資料模型。文獻上估計此種模型通常利用差分來消除固定效果後,再以一般動差法 (generalized method of moments, GMM) 進行估計。然而,這樣的估計方式在序列為近單根或具單根時卻面臨了弱工具變數(weak instrument)的問題,並導致嚴重的估計偏誤。相反的,差分轉換估計式所利用的動差條件在近單根與單根的情況下仍然穩固,因此在小樣本下的估計偏誤相當輕微(甚至無偏誤)。另外,我們證明了不論序列長度(T )或橫斷面規模(n)趨近無窮大,差分轉換估計式皆有漸近常態分配的性質。與單一序列時相同的是,我們提出的二階差分轉換估計式在序列具正自我相關性時的漸近變異數較一階差分轉換估計式小;受惠於此,利用二階差分轉換估計式所建構的檢定具有較佳的檢力。值得注意的是,由於二階差分轉換估計式在單根的情況下仍有漸近常態分配的性質,我們得以直接利用該漸近理論建構追蹤資料單根檢定。電腦模擬結果發現,在小 T 大 n 的情況下,其檢力優於文獻上常用的 IPS 檢定(Im et al., 1997, 2003)。 / This thesis deals with estimation and inference in autoregressive models. Conventionally, the autoregressive models estimated by the least squares (LS) procedure may be subject to two shortcomings. First, the asymptotic distribution of the LS estimates for autoregressive coefficient is discontinuous at unity. Test statistics based on the LS estimates thus follow nonstandard distributions, and the critical values obtained need to rely on Monte Carlo techniques. Secondly, as is well known, the LS estimates of autoregressive models are biased in finite samples. This bias could be substantial and leads to serious size distortion for the test statistics built on the estimates and forecast errors. In this thesis,we consider a simple newmethod ofmoments estimator, termed the “transformed second-difference” (hereafter TSD) estimator, that is without the aforementioned problems, and has many useful applications. Notably, when applied to dynamic panel models, the associated panel unit root tests shares a great power advantage over the existing ones, for the cases with very short time span. The thesis consists of 4 chapters, which are briefly described as follows. 1. Introduction: Overview and Purpose This chapter first reviews the literature and states the purpose of this dissertation. We discuss the sources of problems in estimating autoregressive models with the conventional method. The motivation to estimate the autoregressive series with multiple-difference models, instead of the conventional level model, is provided. We then propose a new estimator, the TSD estimator, which can avoid (fully or partly) the drawbacks of the LS method, and highlight its finite-sample and asymptotic properties. 2. The Bias of 2SLSs and transformed difference estimators in Multiple-Difference AR(1) Models In this chapter, we derive approximate bias for the TSD estimator. For comparisons, the corresponding bias of the two stage least squares estimators (2SLS) in multiple-difference AR(1) models and the transformed first-difference (TFD) estimator proposed by Chowdhurry (1987) are also given as by-products. We find that: (i) All the estimators considered are much less biased than the LS ones with the level regression; (ii)The difference method can be exploited to reduce the bias only up to the order of difference 2; and (iii) The bias of the TFD and TSD estimators share the same order at $O(T^{-1})$ as that of 2SLSs. However, to the extent of bias reductions, neither the 2 considered transformed difference estimators shows a uniform dominance over the entire parameter space. Our simulation evidence lends credible supports to our bias approximation theory. 3. Gaussian Inference in AR(1) Time Series with or without a Unit Root The goal of the chapter is to develop an asymptotic theory of the TSD estimator. Similar to that of the TFD estimator shown by Phillips and Han (2008), the TSDestimator is found to have Gaussian asymptotics for all values of ρ ∈ (−1, 1] with $\sqrt{T}$ rate of convergence, where ρ is the autoregressive coefficient of interest and T is the time span. Specifically, the limit distribution of the TSD estimator is N(0,2) for all possible values of ρ. In addition, the asymptotic variance of the TSD estimator is smaller than that of the TFD estimator for the cases with ρ > 0, and the corresponding t -test thus exhibits superior power to the TFD-based one. 4. Estimation and Inference with Moment Methods for Dynamic Panels with Fixed Effects This chapter demonstrates the usefulness of the TSD estimator when applying to to dynamic panel datamodels. We find again that the TSD estimator displays a standard Gaussian limit, with a convergence rate of $\sqrt{nT}$ for all values of ρ, including unity, irrespective of how n or T approaches infinity. Particularly, the TSD estimator makes use of moment conditions that are strong for all values of ρ, and therefore can completely avoid the weak instrument problem for ρ in the vicinity of unity, and has virtually no finite sample bias. As in the time series case, the asymptotic variance of the TSD estimator is smaller than that of the TFD estimator of Han and Phillips (2009) when ρ > 0 and T > 3, and the corresponding t -ratio test is thus more capable of unveiling the true data generating process. Furthermore, the asymptotic theory can be applied directly to panel unit root test. Our simulation results reveal that the TSD-based unit root test is more powerful than the widely used IPS test (Im et al, 1997, 2003) when n is large and T is small.

Parc marin du Saguenay - Saint-Laurent, Groupe de recherche et d'éducation sur les mammifères marins, GREMM, excursions aux baleines, bélugas, Tadoussac, règlementation, ArcCatalog, ArcMap. / L'important volume des excursions de bateaux touristiques liées aux activités d'observation en mer des mammifères marins (AOM) dans le Parc marin Saguenay-Saint-Laurent (PMSSL) pourrait avoir un impact négatif sur la santé des baleines qui fréquentent cette région de l’estuaire du fleuve Saint-Laurent. Cette situation a poussé les gestionnaires du Parc à désirer un outil d'information et de gestion pour mieux suivre l’évolution des AOM. Le présent mémoire décrit la conception de cet outil en proposant un modèle de l'excursion et des activités d'observation qui sert à l’élaboration d’une base de données. En s’appuyant sur le concept de la trajectoire spatiotemporelle, utilisée pour l'étude du comportement de déplacement basé sur l'activité, et sur le concept de l'objet mobile, développé en géomatique, notre modèle de données permet de reproduire graphiquement les trajectoires des excursions et l'enchaînement des activités qui ont lieu au cours de celles-ci. Le modèle est orienté objet et implanté dans une Geodatabase, une base de données relationnelle exploitable par le système d'information géographique (SIG) ArcGIS. Les objets de la base de données sont créés à partir des données de trois années de suivi des AOM réalisés par le Groupe de recherche et d'éducation sur les mammifères marins (GREMM). Nous montrons que le SIG peut exploiter la base de données de façon à répondre correctement aux besoins en information exprimés par les gestionnaires et que la représentation informatique des excursions sous la forme de trajectoires spatiotemporelles ouvre de nouvelles avenues de recherche sur les patrons d’activités et le comportement de déplacement des bateaux. / The considerable volume of commercial boat trips associated with whale-watching activities (WWAs) in the Saguenay-Saint-Laurent National Marine Park (PMSSL) may have negative impacts on the health of the whales visiting the Saint-Lawrence River estuary. This situation has led park officials to seek an appropriate information management system in order to better monitor WWAs. This paper contributes to the design of such a tool by modeling excursion patterns of WWAs, resulting in a database. Based on the space-time path concept used for the study of activity-based travel behavior as well as the mobile-object geomatical concept, our data-model enables us to plot the trajectories of the excursions and the sequence of activities taking place during those excursions. The model is object-oriented and built into a Geodatabase (an ArcGis-processable relational database). The objects in the database are based on data collected by the Marine Mammal Research and Education Group (GREMM) during a three-year WWA monitoring program. Finally, this report demonstrates that geographic information systems can be effectively used to process the database in a way that serves the needs of park officials, and that the representation of excursion data in a GIS opens up new avenues for research on the space-time pattern of observation activities and the travel behavior of boats.

L'important volume des excursions de bateaux touristiques liées aux activités d'observation en mer des mammifères marins (AOM) dans le Parc marin Saguenay-Saint-Laurent (PMSSL) pourrait avoir un impact négatif sur la santé des baleines qui fréquentent cette région de l’estuaire du fleuve Saint-Laurent. Cette situation a poussé les gestionnaires du Parc à désirer un outil d'information et de gestion pour mieux suivre l’évolution des AOM. Le présent mémoire décrit la conception de cet outil en proposant un modèle de l'excursion et des activités d'observation qui sert à l’élaboration d’une base de données. En s’appuyant sur le concept de la trajectoire spatiotemporelle, utilisée pour l'étude du comportement de déplacement basé sur l'activité, et sur le concept de l'objet mobile, développé en géomatique, notre modèle de données permet de reproduire graphiquement les trajectoires des excursions et l'enchaînement des activités qui ont lieu au cours de celles-ci. Le modèle est orienté objet et implanté dans une Geodatabase, une base de données relationnelle exploitable par le système d'information géographique (SIG) ArcGIS. Les objets de la base de données sont créés à partir des données de trois années de suivi des AOM réalisés par le Groupe de recherche et d'éducation sur les mammifères marins (GREMM). Nous montrons que le SIG peut exploiter la base de données de façon à répondre correctement aux besoins en information exprimés par les gestionnaires et que la représentation informatique des excursions sous la forme de trajectoires spatiotemporelles ouvre de nouvelles avenues de recherche sur les patrons d’activités et le comportement de déplacement des bateaux. / The considerable volume of commercial boat trips associated with whale-watching activities (WWAs) in the Saguenay-Saint-Laurent National Marine Park (PMSSL) may have negative impacts on the health of the whales visiting the Saint-Lawrence River estuary. This situation has led park officials to seek an appropriate information management system in order to better monitor WWAs. This paper contributes to the design of such a tool by modeling excursion patterns of WWAs, resulting in a database. Based on the space-time path concept used for the study of activity-based travel behavior as well as the mobile-object geomatical concept, our data-model enables us to plot the trajectories of the excursions and the sequence of activities taking place during those excursions. The model is object-oriented and built into a Geodatabase (an ArcGis-processable relational database). The objects in the database are based on data collected by the Marine Mammal Research and Education Group (GREMM) during a three-year WWA monitoring program. Finally, this report demonstrates that geographic information systems can be effectively used to process the database in a way that serves the needs of park officials, and that the representation of excursion data in a GIS opens up new avenues for research on the space-time pattern of observation activities and the travel behavior of boats. / Parc marin du Saguenay - Saint-Laurent, Groupe de recherche et d'éducation sur les mammifères marins, GREMM, excursions aux baleines, bélugas, Tadoussac, règlementation, ArcCatalog, ArcMap.

量價關係，一直以來皆為技術分析學派所廣泛運用，其主張運用過去的股價以及成交量來推測股票未來的走勢，而也有許多的研究以及投資策略皆是從量價關係所出。在國內，小型股也由於其股本小的特性，往往成為有心人士炒作之標的。此外，小型股亦較大型股具有不對稱資訊的性質，而由於成交量背後往往隱藏著許多的資訊，因此投資人利用量與價之間的關係，得到能夠有效預測小型股股價的方法以利其投資。 而本文之研究，將量價關係運用在小型股上，想檢視彼此間有無任何關係存在。本文中我們使用了因果關係檢定，三因子模型，以及縱橫迴歸模型，用來分別檢視小型股與大型股的量價關係。驗證結果發現，在不同的檢驗方式下，都會得到小型股較大型股，有顯著量價影響的關係存在。 / The relation between volume and price is widely used in technical analysis. It predicts future stock price by using past stock price and volume. There are lots of investigations and investment strategies are stemmed from it. In Taiwan, small caps are preferred to be held by the people who would like to manipulate the price because of their small number of capitalization. In addition, compared with large caps, small caps are of asymmetric information to the investors. As there is lot of information hidden behind volume, investors are likely to use the relation between volume and price to get a useful way to predict small caps’ stock price. In this paper, I use granger causality test, three-factor model, and panel data model to test the relation between price/return and volume of small caps and big caps separately. The experiment shows that use different ways, we can verify there exist more obvious relations between volume and price in small caps than in large caps.

The aim of the master thesis is to empirically analyze if there is a support for new classics or new Keynesians as a dominant theory of short-run aggregate supply curve. The analysis is based on dynamic panel data model for 38 countries and period between 1970 and 2014. Because the results show some evidence on negative significance of level of inflation in contrast with its variability, I conclude that there is support for the new Keynesian theory. I focus on examination of the panel data assumptions such as the stationarity of explanatory variables, existence of the individual or random effects, validity of homogeneity of slope coefficients and mainly the cross-sectional dependence of error terms. After testing for these assumptions, I choose the most suitable method of estimation for dynamic panel data models. I use these methods for analyzing both linear and non-linear specification of the given model. As a result, we can see that the selection of right estimation method plays a great role in final outcomes. I also check model robustness by including changes of real oil price as a proxy variable for the supply shock in the economy.

Advances in information technology in the past two decades have seen innovations in the field of domestic and industrial computing that led to a paradigm shift in the management and operation of urban water systems by water utility companies. The traditional public management policy that focused on ensuring a minimum quality of service regardless of the costs associated with the processes of catchment, treatment and distribution of water, in many cases even unknown, have evolved towards more efficient cost sensitive models. These new wholly or partly public funded management systems improve not only the quality of service offered to users, but also optimize resources by reducing the cost and causing the minimum environmental impact. The new challenges raised by the European Water Framework1 Directive by imposing cost recovery to improve water efficiency and environmental sustainability have led to a significant change at all levels of water management. Consequently, new priorities have been established in terms of infrastructure management that require the reduction of water losses and the improvement of the water efficiency in urban networks for human consumption. Likewise, in a broader context which includes the water--energy binomial, it is also desirable to improve the energy efficiency and carbon emissions of these systems. Today, network sectoring is the most commonly used strategy to improve management and increase network performance. It basically consists of dividing the network into several smaller hydraulic sectors, where water inlets and outlets are perfectly controlled. This simplifies the task of carrying out periodic water balances in each of the sectors, and allows water loss volume to be assessed for a given period of time. As configuring network sectors is not a trivial task, it is therefore important to have appropriate tools to perform the task efficiently and effectively. Mathematical models can play an important role as decision support tools to help water managers assess the performance of water network distribution systems. This thesis aims to address the current problems of managing urban water networks by combining new information-processing technologies with innovative network modelling techniques. It intends to facilitate the system diagnosis and extend the use of models on the decision-making process to provide better solutions to the management of urban water networks. For this purpose a software extension that works on a geographic information system (GIS) has been developed. It integrates: the hydraulic and water quality simulation program EPANET 2, innovative tools for model analysis and diagnostic, automatic tools for sectoring and computing tools to conduct water balances in the sectors using actual measurements. The work demonstrates the compatibility and complementarity of GIS and hydraulic models as technologies that can be used to support the assessment and diagnosis of water distribution networks. Considering that the majority of information linked to the network system has some geographic reference, it is not surprising that GIS has become a popular tool for dealing with such information. At the same time, the integration of mathematical modelling and simulation tools, offers the GIS a new dimension in the realm of hydraulic study of water networks. Furthermore, if this specific integration is provided with new features aimed not only to facilitate the model building, but also to assist the user in decision-making using powerful algorithms based on the application of the graph theory, the result is a powerful up-to-date analytical tool, which opens up new possibilities in the field of management and efficient operation of urban water supply systems. / Bartolín Ayala, HJ. (2013). Confección de modelos de redes de distribución de agua desde un Sig y desarrollo de herramientas de apoyo a la toma de decisiones [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/33152 / TESIS

This thesis summarizes the information retrieval theory, the relational model basic and focuses on the data indexing in relational database systems. The thesis focuses on multimedia data searching. It includes description of automatic multimedia data content extraction and multimedia data indexing. Practical part discusses design and solution implementation for improving query effectivity for multidimensional vector similarity which describes multimedia data. Thesis final part discusses experiments with this solution.